Jan Hillert, Helmut Butzkueven, Melinda Magyari, Stig Wergeland, Nicholas Moore, Merja Soilu-Hänninen, Tjalf Ziemssen, Jens Kuhle, Luigi Pontieri, Lars Forsberg, Jan Harald Aarseth, Chao Zhu, Nicholas Sicignano, Vasili Mushnikov, Irene Bezemer, Meritxell Sabidó
{"title":"使用多发性硬化症登记处/数据源进行的长期安全性研究中，统一数据质量指标可保证数据质量：CLARION 研究的经验。","authors":"Jan Hillert, Helmut Butzkueven, Melinda Magyari, Stig Wergeland, Nicholas Moore, Merja Soilu-Hänninen, Tjalf Ziemssen, Jens Kuhle, Luigi Pontieri, Lars Forsberg, Jan Harald Aarseth, Chao Zhu, Nicholas Sicignano, Vasili Mushnikov, Irene Bezemer, Meritxell Sabidó","doi":"10.2147/CLEP.S480525","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Understanding the long-term safety of disease-modifying therapies for multiple sclerosis (MS) in routine clinical practice can be undertaken through registry-based studies. However, variability of data quality across such sources poses the challenge of data fit for regulatory decision-making. CLARION, a non-interventional cohort safety study of cladribine tablets, combines aggregated data from MS registries/data sources, except in Germany (which utilizes primary data collection). We describe the application of key data quality indicators (DQIs) within CLARION to evaluate data quality over time, as recommended by the European Medicines Agency (EMA) guideline on registry-based studies.</p><p><strong>Methods: </strong>DQIs were defined with participating registries/sources; they were used to assess data quality according to the EMA Data Quality Framework, addressing consistency, accuracy, completeness, and study representativeness. DQIs were associated with potential remedial measures if data quality was not met. DQIs were summarized overall and for individual MS registries/data sources to November 1, 2022.</p><p><strong>Results: </strong>A total of 28 DQIs were analyzed using data from 5069 patients arising from eight MS registries/data sources and 14 countries. The Representativeness DQIs showed that 72.0% of patients were female, median age at MS diagnosis was 29.0 to 43.3 years, and 93.5% had relapsing-remitting MS. Consistency DQIs showed a total of 2899 patients had achieved at least two years of follow-up; 6.9% did not have any recorded visits during this timeframe. Discrepant values were assessed as part of Accuracy DQIs, and improvements over time were noted for recorded dates of MS onset and diagnosis. Regarding Completeness DQIs, 191/5069 (3.8%) patients were lost to follow-up.</p><p><strong>Conclusion: </strong>The application of 28 DQIs within the CLARION study has helped with understanding, not only intrinsic and question-specific determinants of data quality, but also tracking the quality of post-authorization safety data obtained from MS registries/data sources, thereby providing a foundation for the regulatory decision-making process.</p>","PeriodicalId":10362,"journal":{"name":"Clinical Epidemiology","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11492909/pdf/","citationCount":"0","resultStr":"{\"title\":\"Harmonized Data Quality Indicators Maintain Data Quality in Long-Term Safety Studies Using Multiple Sclerosis Registries/Data Sources: Experience from the CLARION Study.\",\"authors\":\"Jan Hillert, Helmut Butzkueven, Melinda Magyari, Stig Wergeland, Nicholas Moore, Merja Soilu-Hänninen, Tjalf Ziemssen, Jens Kuhle, Luigi Pontieri, Lars Forsberg, Jan Harald Aarseth, Chao Zhu, Nicholas Sicignano, Vasili Mushnikov, Irene Bezemer, Meritxell Sabidó\",\"doi\":\"10.2147/CLEP.S480525\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Understanding the long-term safety of disease-modifying therapies for multiple sclerosis (MS) in routine clinical practice can be undertaken through registry-based studies. However, variability of data quality across such sources poses the challenge of data fit for regulatory decision-making. CLARION, a non-interventional cohort safety study of cladribine tablets, combines aggregated data from MS registries/data sources, except in Germany (which utilizes primary data collection). We describe the application of key data quality indicators (DQIs) within CLARION to evaluate data quality over time, as recommended by the European Medicines Agency (EMA) guideline on registry-based studies.</p><p><strong>Methods: </strong>DQIs were defined with participating registries/sources; they were used to assess data quality according to the EMA Data Quality Framework, addressing consistency, accuracy, completeness, and study representativeness. DQIs were associated with potential remedial measures if data quality was not met. DQIs were summarized overall and for individual MS registries/data sources to November 1, 2022.</p><p><strong>Results: </strong>A total of 28 DQIs were analyzed using data from 5069 patients arising from eight MS registries/data sources and 14 countries. The Representativeness DQIs showed that 72.0% of patients were female, median age at MS diagnosis was 29.0 to 43.3 years, and 93.5% had relapsing-remitting MS. Consistency DQIs showed a total of 2899 patients had achieved at least two years of follow-up; 6.9% did not have any recorded visits during this timeframe. Discrepant values were assessed as part of Accuracy DQIs, and improvements over time were noted for recorded dates of MS onset and diagnosis. Regarding Completeness DQIs, 191/5069 (3.8%) patients were lost to follow-up.</p><p><strong>Conclusion: </strong>The application of 28 DQIs within the CLARION study has helped with understanding, not only intrinsic and question-specific determinants of data quality, but also tracking the quality of post-authorization safety data obtained from MS registries/data sources, thereby providing a foundation for the regulatory decision-making process.</p>\",\"PeriodicalId\":10362,\"journal\":{\"name\":\"Clinical Epidemiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11492909/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Epidemiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/CLEP.S480525\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Epidemiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/CLEP.S480525","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Harmonized Data Quality Indicators Maintain Data Quality in Long-Term Safety Studies Using Multiple Sclerosis Registries/Data Sources: Experience from the CLARION Study.
Purpose: Understanding the long-term safety of disease-modifying therapies for multiple sclerosis (MS) in routine clinical practice can be undertaken through registry-based studies. However, variability of data quality across such sources poses the challenge of data fit for regulatory decision-making. CLARION, a non-interventional cohort safety study of cladribine tablets, combines aggregated data from MS registries/data sources, except in Germany (which utilizes primary data collection). We describe the application of key data quality indicators (DQIs) within CLARION to evaluate data quality over time, as recommended by the European Medicines Agency (EMA) guideline on registry-based studies.
Methods: DQIs were defined with participating registries/sources; they were used to assess data quality according to the EMA Data Quality Framework, addressing consistency, accuracy, completeness, and study representativeness. DQIs were associated with potential remedial measures if data quality was not met. DQIs were summarized overall and for individual MS registries/data sources to November 1, 2022.
Results: A total of 28 DQIs were analyzed using data from 5069 patients arising from eight MS registries/data sources and 14 countries. The Representativeness DQIs showed that 72.0% of patients were female, median age at MS diagnosis was 29.0 to 43.3 years, and 93.5% had relapsing-remitting MS. Consistency DQIs showed a total of 2899 patients had achieved at least two years of follow-up; 6.9% did not have any recorded visits during this timeframe. Discrepant values were assessed as part of Accuracy DQIs, and improvements over time were noted for recorded dates of MS onset and diagnosis. Regarding Completeness DQIs, 191/5069 (3.8%) patients were lost to follow-up.
Conclusion: The application of 28 DQIs within the CLARION study has helped with understanding, not only intrinsic and question-specific determinants of data quality, but also tracking the quality of post-authorization safety data obtained from MS registries/data sources, thereby providing a foundation for the regulatory decision-making process.
期刊介绍：
Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment.
Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews.
Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews.
When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes.
The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.
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